CoraL: interpretable contrastive meta-learning for the prediction of cancer-associated ncRNA-encoded small peptides.

NcRNA-encoded small peptides (ncPEPs) have recently emerged as promising targets and biomarkers for cancer immunotherapy. Therefore, identifying cancer-associated ncPEPs is crucial for cancer research. In this work, we propose CoraL, a novel supervised contrastive meta-learning framework for predicting cancer-associated ncPEPs. Specifically, the proposed meta-learning strategy enables our model to learn meta-knowledge from different types of peptides and train a promising predictive model even with few labeled samples. The results show that our model is capable of making high-confidence predictions on unseen cancer biomarkers with only five samples, potentially accelerating the discovery of novel cancer biomarkers for immunotherapy. Moreover, our approach remarkably outperforms existing deep learning models on 15 cancer-associated ncPEPs datasets, demonstrating its effectiveness and robustness. Interestingly, our model exhibits outstanding performance when extended for the identification of short open reading frames derived from ncPEPs, demonstrating the strong prediction ability of CoraL at the transcriptome level. Importantly, our feature interpretation analysis discovers unique sequential patterns as the fingerprint for each cancer-associated ncPEPs, revealing the relationship among certain cancer biomarkers that are validated by relevant literature and motif comparison. Overall, we expect CoraL to be a useful tool to decipher the pathogenesis of cancer and provide valuable information for cancer research. The dataset and source code of our proposed method can be found at https://github.com/Johnsunnn/CoraL.

PLPMpro: Enhancing promoter sequence prediction with prompt-learning based pre-trained language model.

The promoter region, positioned proximal to the transcription start sites, exerts control over the initiation of gene transcription by modulating the interaction with RNA polymerase. Consequently, the accurate recognition of promoter regions represents a critical focus within the bioinformatics domain. Although some methods leveraging pre-trained language models (PLMs) for promoter prediction have been proposed, the full potential of such PLMs remains largely untapped. In this study, we introduce PLPMpro, a model that capitalizes on prompt-learning and the pre-trained language model to enhance the prediction of promoter sequences. PLPMpro effectively harnesses the prompt learning paradigm to fully exploit the inherent capacities of the PLM, resulting in substantial improvements in prediction performance. Experiment results unequivocally demonstrate the efficacy of prompt learning in bolstering the capabilities of the pre-trained model. Consequently, PLPMpro surpasses both typical pre-trained model-based methods for promoter prediction and typical deep learning methods. Furthermore, we conduct various experiments to meticulously scrutinize the effects of different prompt learning settings and different numbers of soft modules on the model performance. More importantly, the interpretation experiment reveals that the pre-trained model captures biological semantics. Collectively, this research imparts a novel perspective on the optimal utilization of PLMs for addressing biological problems.

ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides.

MOTIVATION: Plant Small Secreted Peptides (SSPs) play an important role in plant growth, development, and plant-microbe interactions. Therefore, the identification of SSPs is essential for revealing the functional mechanisms. Over the last few decades, machine learning-based methods have been developed, accelerating the discovery of SSPs to some extent. However, existing methods highly depend on handcrafted feature engineering, which easily ignores the latent feature representations and impacts the predictive performance. RESULTS: Here, we propose ExamPle, a novel deep learning model using Siamese network and multi-view representation for the explainable prediction of the plant SSPs. Benchmarking comparison results show that our ExamPle performs significantly better than existing methods in the prediction of plant SSPs. Also, our model shows excellent feature extraction ability. Importantly, by utilizing in silicomutagenesis experiment, ExamPle can discover sequential characteristics and identify the contribution of each amino acid for the predictions. The key novel principle learned by our model is that the head region of the peptide and some specific sequential patterns are strongly associated with the SSPs' functions. Thus, ExamPle is expected to be a useful tool for predicting plant SSPs and designing effective plant SSPs. AVAILABILITY AND IMPLEMENTATION: Our codes and datasets are available at https://github.com/Johnsunnn/ExamPle.

MDL-CPI: Multi-view deep learning model for compound-protein interaction prediction.

Elucidating the mechanisms of Compound-Protein Interactions (CPIs) plays an essential role in drug discovery and development. Many computational efforts have been done to accelerate the development of this field. However, the current predictive performance is still not satisfactory, and existing methods consider only protein and compound features, ignoring their interactive information. In this study, we propose a multi-view deep learning method named MDL-CPI for CPI prediction. To sufficiently extract discriminative information, we introduce a hybrid architecture that leverages BERT (Bidirectional Encoder Representations from Transformers) and CNN (Convolutional Neural Network) to extract protein features from a sequential perspective, use the GNN (Graph Neural Networks) to extract compound features from a structural perspective, and generate a unified feature space by using AE2 (Autoencoder in Autoencoder Networks) network to learn the interactive information between BERT-CNN and Graph embeddings. Comparative results on benchmark datasets show that our proposed method exhibits better performance compared to existing CPI prediction methods, demonstrating the strong predictive ability of our model. Importantly, we demonstrate that the learned interactive information between compounds and proteins is critical to improve predictive performance. We release our source code and dataset at: https://github.com/Longwt123/MDL-CPI.